1. Assessment of regional innovation
potential in Russia
Prepared by Stepan Zemtsov
PhD student
Supervised by Vyacheslav Baburin
Professor
Lomonosov Moscow
State University
Faculty of Geography
Department of economic and social geography
of Russia

2. Object, hypothesis, purpose
• Object – innovation space of Russia, its territorial
structure and dynamics (in terms of innovation
potential)
• Hypothesis – Russian innovation space has a
relatively stable structure, despite negative trends of
decreasing density. This structure corresponds to
global trends and laws, but it has features of territorial
organization inherited from the Soviet period
• Purpose – to identify regions with the highest
potential, where support of innovation activities would
be the most effective

3. Structure
I. Theoretical background
II. Generation of innovation (assessment of
innovation potential)
III. Diffusion of innovation (assessment of
innovativeness)
IV. Regional innovation clusters (assessment of
potential and territorial priorities)
V. Conclusion

4. I.1. Conceptual framework
Socio-economic space
Territorial socio-economic
system
Regional innovation system
Innovation cycle

5. I.2. Terminology
• Innovation cycle - institutionalized process of
transition from ‘idea generation’ stage to the stage of
‘commercial product’ and ‘consumption’
• Social economic space (SESP) - general
conditions of the region, especially its economicgeographical position
• Territorial social-economic system (TSES) - a
set of interrelated technologies, people,
organizations, institutions and ideas in a certain area
• Regional innovation system (RIS) infrastructural, institutional and organizational
embodiment of innovation cycle stages

6. I.3. Scheme of territorial social-economic
system
Informational (cognitive) sphere
Economic sphere
Social sphere
Psychological
sphere
Political sphere
Cultural sphere
Technological basement

7. I.4. Scheme of RIS
(based on innovation cycle)
Financial infrastructure
Administrative and legal infrastructure
Venture capitalists, Regional Venture Fund, business
angels, banks, grants
Innovation Department of the Administration of the region.
Governor Council on Innovation at.
Legal and regulatory framework.
Innovation strategy and programs
Education
High educational
centres, centres
for e-learning,
retraining centres
Science
Transfer
(R’n’D)
Production
Russian Academy of
Science, research
universities, research
centres
R’n’D centres,
technology transfer
centres, engineering
centres, small
innovation
companies
Industrial and
service entities,
creative industries
RIS core
Consumption
Military-industrial
complex,
government,
trans-national
corporations,
households
Technological infrastructure
Informational infrastructure
Technology parks, business incubators, centers of
excellence
Innovation portal, organization of conferences, branding
and marketing

8. I.5. Generation of innovation
National innovation system
Bengt-Åke Lundvall
(1941 - …)
Richard Nelson
(1930 - …)
Cluster
Mickael Porter
(1941 - …)
Regional innovation system
Bjørn Asheim
(1948 - …)
Creative class
Richard Florida
(1957 - …)

9. I.6. Diffusion and transfer of innovation
Social science
Diffusion of innovation
(Diffusion of
innovation, 1962)
Spatial science
Technology
transfer
(Technological
forecasting in
perspective,
1967).
Everett Rogers
(1931 – 2004)
Spatial diffusion
(Innovation
diffusion as a
spatial process,
1953)
Torsten Hägerstrand
(1916 – 2004)
Erich Jantsch
(1929 – 1980)
Core-periphery
(Regional
Development
Policy, 1967)
John Friedman
(1926 - …)

10. II.1. Russian national innovation system
Index
Russian
Number of
position in
countries
2009
Innovation Index
WB
41
145
Innovation
Capacity Index
49
130
Patents
Scientists
Global Innovation
Index INSEAD
68
130
Innovation Index
WEF
73
133
High-tech export

11. II.2. Patent activity as a main indicator of
innovation space
Equation of potential field (gravity model)
where Pj is a value of an indicator (number of granted
patents per 100 000 urban citizens) in point j, Рі is a
value of the indicator in a point i; Dji is a distance from
a point j to a point i, km.
Equation for territorial diversity of innovation
activity between regions (Shannon entropy):
where Sі - a percentage of granted patents in a region i
of the total number of granted patents in Russia.

13. II.4. Concentration of innovation activity

14. II.5. Creative potential (index of creativity)
(according to R. Florida, A. Pilyasov)
1. Subindex of talent:
• human capital (percentage of employees with higher
education, %)
• scientific talent (number of researchers per 1 million
inhabitants)
2. Subindex of technology:
• science investment (R & D expenditure per GRP, %)
• patent activity (number of patents granted per million
inhabitants)
3. Subindex of tolerance:
• ethnical diversity (percentage of households, where members
are of different ethnic group, %)
• international attractiveness (percentage of migrants from
outside Russia in total arrivals, %; number of migrants per 10
thousand inhabitants)

15. II.6. Clusters of regions by creativity index

16. II.7. Factor analysis
• 38 indicators, based on expert interviews and
existing literature, were divided by SESP, TSES and
RIS subcategories
• Factor, correlation and normal distribution analysis
• Each indicator either
increases the
probability of
innovation
generation, or an
indicator of
innovation activity
itself

17. Socio-economic space
Territorial socioeconomic system
Regional innovation
system
1.1. Economic-geographical position (capital, agglomeration, coastal area)
1.2. Population density
1.3. Percentage of urban citizens (urabnization )
1.4. Percentage of population in cities with more than 200 th. people
Technological sphere
2.1. Percentage of ICT expenditure in GDP
2.2. Computers per capita
2.3. Computers with Internet per capita
2.4. Percentage of organizations with web-site
2.5. Percentage of organizations with special programs
Economic sphere
3. GDP per capita
Social sphere
4.1. Percentage of people with high education
4.2. Migration per capita
4.3. Percentage of foreign migrants
Cultural sphere
5.1. Percentage of households, where members are of different ethnic group
Informational sphere
6.1. Percentage of Internet users
Education
7.1. Number of university students per capita
Science
8.1. Number of scientists per capita
8.2. Number of registered patents per 1000 employees
Transfer (R’n’D)
9.1. Percentage of employees in R & D sector in total employment
9.2. Percentage of R’n’D expenditure in GDP
9.3. Percentage of R’n’D organizations
Production
10.1. Percentage of technological innovations expenditure in GDP
10.2. Number of new technologies per 1000 employees
10.3. Percentage of innovation active organizations
10.4. Innovative production percentage in total production
Consumption

18. II.9. Index of innovation potential
The first factor (Index of absorption): urbanization (%), computers with
Internet access per 100 employees, GDP per capita, percentage of
multinational families (%), percentage of Internet-users (%), and mobile
phones per capita.
The second factor (Index of innovation potential):
SESP:
• economic-geographical position (points)
TSES:
• percentage of residents in cities with population more than 200 thousand
people (%)
• percentage of people with a higher education in the population (%)
RIS:
• number of university students per 10 thousand people
• percentage of employees in R & D sector in total employment (%)
• number of registered patents per 1000 employees
• percentage of organizations with a website (%)

19. II.10. Clusters of integral innovation
potential

20. II.11. Potential field of integral index

21. II.12. Assessment of development potential
of regional innovation systems

22. III.1. Spatial diffusion of innovation
Mobile phones usage, or subscriptions (active SIM
cards per 100 people) per capita

23. III.2. Clusters by diffusion of innovation

24. III.3. Bass model (Bass, 1969)
Bass considered a population of Nmax
individuals who are both innovators
(those with a constant propensity to
purchase, a) and imitators (those whose
propensity to purchase is influenced by the
amount of previous adopters, b) in socalled mixed-influence model The model
can be rewritten from original differential
form in terms of its discrete analogue
N(t+1) – N(t) = a*Nmax + (b*Nmax – a) *N(t) – b*N(t)2 =
A1 + A2*N(t) + A3* N(t)2 + e(t)
where a = A1/Nmax, b = – A3* Nmax, Nmax = (–A2±√(A22 –4*A1*A3))/2*A3)

25. III.4. Estimation of a and b

26. III.5. Innovativeness

27. III.6. Priorities for regional innovation policy

28. IV.1. Regional innovation clusters in ‘Environmental
management’
130 organizations: two universities – forecasting centres and 12 universities – members of the
network, interacting with outside universities (12 organizations), research organizations (42) and entities (62)
The index of competence ( I KMP )
I KMP  ( I C  ( I NT  I VTZ ))
– subindex of the number of university competencies, I NT – subindex of new technologies,
where I C
IVTZ – subindex of transfer centres
The index of interaction( IVZ )
I VZ  I SV  I TR  I SR ,
where I SV – subindex of the number of associated organization (or interactions), I TR – Shannon
index of the share of connections between different cities, I SR – Shannon index of the share of
organizations of different stages of the innovation cycle.

29. IV.2. Regional and interregional innovation
clusters in ‘Environmental management’

30. V. Conclusion
• Russian innovation space can be described by core-periphery model:
the largest cities are the centres for generation and diffusion of innovation
on the northern and southern agrarian peripheries.
• After the collapse of the Soviet Union the innovation space was divided
into a number of isolated and poorly connected centres,
concentration increased, variety of functions declined, and "lifeless"
periphery was formed. These negative processes have not been overcome,
despite the economic achievements of the 2000s.
• Hierarchical model of diffusion from the main centres to secondary
prevails in Russia. Factor of geographical location (borderlands and
seaside location) also play a crucial role. At the initial stage, many regions
have similar level of saturation (parameter a), but further absorption
stops in the northern regions due to the low population density, and in the
southern regions because of agricultural specialization and high
institutional barriers.
• High correlation between the territorial structure of urban settlements,
innovation potential of cities and new innovation centres was identified.
The territorial structure and the potential of the clusters indicate the
possible direction of the shift of innovative activity.

31. References
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32. Thank you for your attention!
Спасибо за внимание!